000148306 001__ 148306
000148306 005__ 20250115160156.0
000148306 0247_ $$2doi$$a10.1016/j.jocs.2024.102507
000148306 0248_ $$2sideral$$a141723
000148306 037__ $$aART-2025-141723
000148306 041__ $$aeng
000148306 100__ $$0(orcid)0000-0003-1270-5852$$aHernandez, Monica$$uUniversidad de Zaragoza
000148306 245__ $$aPDE-LDDMM meets NODEs: Introducing neural ordinary differential equation solvers in PDE-constrained Large Deformation Diffeomorphic Metric Mapping
000148306 260__ $$c2025
000148306 5060_ $$aAccess copy available to the general public$$fUnrestricted
000148306 5203_ $$aNon-rigid image registration is a crucial task in various medical applications, allowing the alignment of images with complex spatial or temporal variations. This paper introduces NODEO-LDDMM and NODEO-PDE-LDDMM, two innovative deep-learning-based approaches that bridge the gap between Large Deformation Diffeomorphic Metric Mapping (LDDMM) and neural ordinary differential equations (NODEs). LDDMM and PDE-LDDMM offer mathematically well-established formulations for diffeomorphic registration, while NODEs provide the flexibility of deep-learning in the solution of the ODEs involved in both methods. Both NODEO-LDDMM and NODEO-PDE-LDDMM include the strengths of deep-learning into LDDMM, enabling a robust optimization with a good balance between accuracy and transformation smoothness in their solutions. Our proposed methods reached or outperformed their traditional counterparts and the nearly diffeomorphic deep-learning-based approaches selected as benchmarks. This work contributes to advancing non-rigid image registration techniques, with a methodology suited to overcome some of the limitations of deep-learning in medical image registration.
000148306 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T64-23R$$9info:eu-repo/grantAgreement/ES/ISCIII/RD24-0007-0022$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104358RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-138703OB-I00
000148306 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000148306 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000148306 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000148306 773__ $$g85 (2025), 102507 18 pp.]$$pJ. comput. sci.$$tJournal of computational science$$x1877-7503
000148306 8564_ $$s14738378$$uhttps://zaguan.unizar.es/record/148306/files/texto_completo.pdf$$yVersión publicada
000148306 8564_ $$s2645429$$uhttps://zaguan.unizar.es/record/148306/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000148306 909CO $$ooai:zaguan.unizar.es:148306$$particulos$$pdriver
000148306 951__ $$a2025-01-15-15:06:55
000148306 980__ $$aARTICLE